issue_comments: 565733023

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https://github.com/pydata/xarray/issues/3349#issuecomment-565733023 https://api.github.com/repos/pydata/xarray/issues/3349 565733023 MDEyOklzc3VlQ29tbWVudDU2NTczMzAyMw== 81219 2019-12-14T16:43:14Z 2019-12-14T16:43:14Z CONTRIBUTOR

@maboualidev Nice ! I see you're storing the residuals in the DataArray attributes. From my perspective, it would be useful to have those directly as DataArrays. Thoughts ?

So it looks like there are multiple inspirations to draw from. Here is what I could gather.

  • xscale.signal.fitting.polyfit(obj, deg=1, dim=None, coord=None) supports chunking along the fitting dimension using dask.array.linalg.lstsq. No explicit missing data handling.
  • xyzpy.signal.xr_polyfit(obj, dim, ix=None, deg=0.5, poly='hermite') applies np.polynomial.polynomial.polyfit using xr.apply_ufunc along dim with the help of numba. Also supports other types of polynomial (legendre, chebyshev, ...). Missing values are masked out 1D wise.
  • geocat.comp.ndpolyfit(x: Iterable, y: Iterable, deg: int, axis: int = 0, **kwargs) -> (xr.DataArray, da.Array) reorders the array to apply np.polyfit along dim, returns the full outputs (residuals, rank, etc) as DataArray attributes. Missing values are masked out in bulk if possible, 1D-wise otherwise.

There does not seem to be matching polyval implementations for any of those nor support for indexing along a time dimension with a non-standard calendar. 

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